{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Introducing Scikit-Learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "<!--BOOK_INFORMATION-->\n",
    "\n",
    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
    "\n",
    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "<!--NAVIGATION-->\n",
    "< [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb) | [Contents](Index.ipynb) | [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Python machine learning \n",
    "\n",
    "- [Scikit-Learn](http://scikit-learn.org) provides efficient versions of a large number of common algorithms.\n",
    "    - Scikit-Learn is characterized by a clean, uniform, and streamlined API, as well as by very useful and complete online documentation.\n",
    "\n",
    "> # Once you understand the basic use and syntax of Scikit-Learn for one type of model, switching to a new model or algorithm is very straightforward.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Python machine learning \n",
    "\n",
    "A solid understanding of these API elements will form the foundation for understanding the deeper practical discussion of machine learning algorithms and approaches.\n",
    "\n",
    "This section provides an overview of the Scikit-Learn API \n",
    "\n",
    "- The *data representation* in Scikit-Learn\n",
    "- The *Estimator* API\n",
    "    - a more interesting example of using these tools for exploring a set of images of hand-written digits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Data Representation in Scikit-Learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Machine learning is about creating models from data: \n",
    "- How data can be represented in order to be understood by the computer.\n",
    "    - Tables of data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Data as table\n",
    "\n",
    "A basic table is a two-dimensional grid of data\n",
    "- the rows represent individual elements of the dataset\n",
    "- the columns represent quantities related to each of these elements.\n",
    "\n",
    "For example, consider the [Iris dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set), famously analyzed by Ronald Fisher in 1936.\n",
    "\n",
    "We can download this dataset in the form of a Pandas ``DataFrame`` using the [seaborn](http://seaborn.pydata.org/) library:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:13.808337Z",
     "start_time": "2019-06-19T03:32:13.789422Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set_context(\"talk\", font_scale=1.5)\n",
    "\n",
    "iris = sns.load_dataset('iris')\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "- Each row of the data refers to a single observed flower\n",
    "- The number of rows is the total number of flowers in the dataset.\n",
    "    - the rows of the matrix as *samples*\n",
    "    - the number of rows as ``n_samples``.\n",
    "- each column of the data refers to a particular quantitative piece of information that describes each sample.\n",
    "    - the columns of the matrix as *features*\n",
    "    - the number of columns as ``n_features``."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Features matrix\n",
    "\n",
    "This table layout of the information can be thought of as a ``two-dimensional numerical array or matrix``, which we will call the **features matrix**.\n",
    "\n",
    "- The features matrix is often stored in a variable named ``X``.\n",
    "- The features matrix is assumed to be two-dimensional, with shape ``[n_samples, n_features]``, \n",
    "- The features matrix is most often contained in a NumPy array or a Pandas ``DataFrame``\n",
    "- some Scikit-Learn models also accept SciPy sparse matrices.\n",
    "\n",
    "The samples (i.e., rows) always refer to the individual objects described by the dataset.\n",
    "- For example, the sample might be a flower, a person, a document, an image, a sound file, a video, an astronomical object, or anything else you can describe with a set of quantitative measurements.\n",
    "\n",
    "The features (i.e., columns) always refer to the distinct observations that describe each sample in a quantitative manner.\n",
    "- Features are generally real-valued, but may be Boolean or discrete-valued in some cases."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Target array\n",
    "\n",
    "In addition to the feature matrix ``X``, we also generally work with a *label* or *target* array, which by convention we will usually call ``y``.\n",
    "- The target array is usually one dimensional, with length ``n_samples``\n",
    "- The target array is generally contained in a NumPy array or Pandas ``Series``.\n",
    "- The target array may have continuous numerical values, or discrete classes/labels.\n",
    "\n",
    "While some Scikit-Learn estimators do handle multiple target values in the form of a two-dimensional, ``[n_samples, n_targets]`` target array, we will primarily be working with the common case of a one-dimensional target array."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Target array\n",
    "\n",
    "The target array is usually the quantity we want to *predict from the data*: in statistical terms, it is the dependent variable.\n",
    "> For example, in the preceding data we may wish to construct a model that can predict the species of flower based on the other measurements; in this case, the ``species`` column would be considered the target array.\n",
    "\n",
    "With this target array in mind, we can use Seaborn (see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)) to conveniently visualize the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:18.031086Z",
     "start_time": "2019-06-19T03:32:13.943574Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 850.275x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import seaborn as sns; \n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "sns.set()\n",
    "sns.set_context(\"talk\", font_scale=1)\n",
    "sns.pairplot(iris, hue='species', size=2.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "For use in Scikit-Learn, we will extract the features matrix and target array from the ``DataFrame``\n",
    "- we can use some of the Pandas ``DataFrame`` operations discussed in the [Chapter 3](03.00-Introduction-to-Pandas.ipynb):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:18.041483Z",
     "start_time": "2019-06-19T03:32:18.033309Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_iris = iris.drop('species', axis=1)\n",
    "X_iris.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:18.054656Z",
     "start_time": "2019-06-19T03:32:18.043802Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width\n",
       "0           5.1          3.5           1.4          0.2\n",
       "1           4.9          3.0           1.4          0.2\n",
       "2           4.7          3.2           1.3          0.2"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_iris[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:18.061918Z",
     "start_time": "2019-06-19T03:32:18.056674Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150,)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_iris = iris['species']\n",
    "y_iris.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:18.070751Z",
     "start_time": "2019-06-19T03:32:18.064671Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    setosa\n",
       "1    setosa\n",
       "2    setosa\n",
       "Name: species, dtype: object"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_iris[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "To summarize, the expected layout of features and target values is visualized in the following diagram:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "![](figures/05.02-samples-features.png)\n",
    "[figure source in Appendix](06.00-Figure-Code.ipynb#Features-and-Labels-Grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Scikit-Learn's Estimator API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "With this data properly formatted, we can move on to consider the *estimator* API of Scikit-Learn:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Guiding principles outlined in the [Scikit-Learn API paper](http://arxiv.org/abs/1309.0238):\n",
    "\n",
    "- *Consistency*: All objects share a common interface drawn from a limited set of methods, with consistent documentation.\n",
    "    - Every machine learning algorithm in Scikit-Learn is implemented via the Estimator API, which provides a consistent interface for a wide range of machine learning applications.\n",
    "\n",
    "- *Inspection*: All specified parameter values are exposed as public attributes.\n",
    "\n",
    "- *Limited object hierarchy*: \n",
    "    - Only algorithms are represented by Python classes; \n",
    "    - datasets are represented in standard formats (NumPy arrays, Pandas ``DataFrame``s, SciPy sparse matrices) and \n",
    "    - parameter names use standard Python strings.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Guiding principles outlined in the [Scikit-Learn API paper](http://arxiv.org/abs/1309.0238):\n",
    "\n",
    "- *Composition*: Many machine learning tasks can be expressed as sequences of more fundamental algorithms,\n",
    "  and Scikit-Learn makes use of this wherever possible.\n",
    "\n",
    "- *Sensible defaults*: When models require user-specified parameters, the library defines an appropriate default value.\n",
    "\n",
    "> In practice, these principles make Scikit-Learn very easy to use, once the basic principles are understood.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Basics of the API\n",
    "\n",
    "Most commonly, the steps in using the Scikit-Learn estimator API are as follows\n",
    "(we will step through a handful of detailed examples in the sections that follow).\n",
    "\n",
    "1. Choose a class of model by importing the appropriate estimator class from Scikit-Learn.\n",
    "2. Choose model hyperparameters by instantiating this class with desired values.\n",
    "3. Arrange data into a features matrix and target vector following the discussion above.\n",
    "4. Fit the model to your data by calling the ``fit()`` method of the model instance.\n",
    "5. Apply the Model to new data:\n",
    "   - For supervised learning, often we predict labels for unknown data using the ``predict()`` method.\n",
    "   - For unsupervised learning, we often transform or infer properties of the data using the ``transform()`` or ``predict()`` method.\n",
    "\n",
    "We will now step through several simple examples of applying supervised and unsupervised learning methods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Supervised learning example: Simple linear regression\n",
    "\n",
    "As an example of this process, let's consider a simple linear regression—that is, the common case of fitting a line to $(x, y)$ data.\n",
    "We will use the following simple data for our regression example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:18.327436Z",
     "start_time": "2019-06-19T03:32:18.073935Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "rng = np.random.RandomState(42)\n",
    "x = 10 * rng.rand(50)\n",
    "y = 2 * x - 1 + rng.randn(50)\n",
    "plt.scatter(x, y) \n",
    "plt.xlabel('x', fontsize = 30)\n",
    "plt.ylabel('y', fontsize = 30);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "With this data in place, we can use the recipe outlined earlier. Let's walk through the process: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### 1. Choose a class of model\n",
    "\n",
    "In Scikit-Learn, every class of model is represented by a Python class.\n",
    "So, for example, if we would like to compute a simple linear regression model, we can import the linear regression class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:18.335842Z",
     "start_time": "2019-06-19T03:32:18.330854Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Note that other more general linear regression models exist as well; you can read more about them in the [``sklearn.linear_model`` module documentation](http://Scikit-Learn.org/stable/modules/linear_model.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### 2. Choose model hyperparameters\n",
    "\n",
    "An important point is that *a class of model is not the same as an instance of a model*.\n",
    "\n",
    "Once we have decided on our model class, there are still some options open to us.\n",
    "Depending on the model class we are working with, we might need to answer one or more questions like the following:\n",
    "\n",
    "- Would we like to fit for the offset (i.e., *y*-intercept)?\n",
    "- Would we like the model to be normalized?\n",
    "- Would we like to preprocess our features to add model flexibility?\n",
    "- What degree of regularization would we like to use in our model?\n",
    "- How many model components would we like to use?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### 2. Choose model hyperparameters\n",
    "\n",
    "\n",
    "These are examples of the important choices that must be made *once the model class is selected*.\n",
    "- These choices are often represented as *hyperparameters*, or parameters that must be set before the model is fit to data.\n",
    "- In Scikit-Learn, hyperparameters are chosen by passing values at model instantiation.\n",
    "\n",
    "We will explore how you can quantitatively motivate the choice of hyperparameters in [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb).\n",
    "\n",
    "For our linear regression example, we can instantiate the ``LinearRegression`` class and specify that we would like to fit the intercept using the ``fit_intercept`` hyperparameter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:18.346743Z",
     "start_time": "2019-06-19T03:32:18.338968Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression(fit_intercept=True)\n",
    "model\n",
    "#help(LinearRegression)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Keep in mind that when the model is instantiated, the only action is the storing of these hyperparameter values.\n",
    "- In particular, we have not yet applied the model to any data: \n",
    "- the Scikit-Learn API makes very clear the distinction between *choice of model* and *application of model to data*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### 3. Arrange data into a features matrix and target vector\n",
    "\n",
    "Previously we detailed the Scikit-Learn data representation, which requires a two-dimensional features matrix and a one-dimensional target array.\n",
    "\n",
    "- The target variable ``y`` is already in the correct form (a length-``n_samples`` array)\n",
    "- The feature matrix ``x`` should be transformed to a matrix of size ``[n_samples, n_features]``.\n",
    "\n",
    "In this case, this amounts to a simple reshaping of the one-dimensional array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:18.356269Z",
     "start_time": "2019-06-19T03:32:18.349760Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 1)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = x[:, np.newaxis]\n",
    "X.shape "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### 4. Fit the model to your data\n",
    "\n",
    "Now it is time to apply our model to data.\n",
    "This can be done with the ``fit()`` method of the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:18.364911Z",
     "start_time": "2019-06-19T03:32:18.358850Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "This ``fit()`` command causes a number of model-dependent internal computations to take place, and the results of these computations are stored in model-specific attributes that the user can explore.\n",
    "\n",
    "In Scikit-Learn, by convention all model parameters that were learned during the ``fit()`` process have trailing underscores; \n",
    "- for example in this linear model, we have the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:18.372356Z",
     "start_time": "2019-06-19T03:32:18.367717Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.9776566])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The parameters represent the slope of the simple linear fit to the data.\n",
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:18.379876Z",
     "start_time": "2019-06-19T03:32:18.375226Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.9033107255311164"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The parameter represent the intercept of the simple linear fit to the data.\n",
    "model.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Comparing to the data definition, we see that they are very close to the input slope of 2 and intercept of -1.\n",
    "\n",
    "One question that frequently comes up regards the uncertainty in such internal model parameters.\n",
    "\n",
    "In general, Scikit-Learn does not provide tools to draw conclusions from internal model parameters themselves:\n",
    "- interpreting model parameters is much more a *statistical modeling* question than a *machine learning* question.\n",
    "- Machine learning rather focuses on what the model *predicts*.\n",
    "\n",
    "If you would like to dive into the meaning of fit parameters within the model, other tools are available, including the [Statsmodels Python package](http://statsmodels.sourceforge.net/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### 5. Predict labels for unknown data\n",
    "\n",
    "Once the model is trained, the main task of supervised machine learning is to evaluate it based on what it says about new data that was not part of the training set.\n",
    "In Scikit-Learn, this can be done using the ``predict()`` method.\n",
    "For the sake of this example, our \"new data\" will be a grid of *x* values, and we will ask what *y* values the model predicts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:18.389410Z",
     "start_time": "2019-06-19T03:32:18.382166Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.        , -0.75510204, -0.51020408, -0.26530612, -0.02040816,\n",
       "        0.2244898 ,  0.46938776,  0.71428571,  0.95918367,  1.20408163,\n",
       "        1.44897959,  1.69387755,  1.93877551,  2.18367347,  2.42857143,\n",
       "        2.67346939,  2.91836735,  3.16326531,  3.40816327,  3.65306122,\n",
       "        3.89795918,  4.14285714,  4.3877551 ,  4.63265306,  4.87755102,\n",
       "        5.12244898,  5.36734694,  5.6122449 ,  5.85714286,  6.10204082,\n",
       "        6.34693878,  6.59183673,  6.83673469,  7.08163265,  7.32653061,\n",
       "        7.57142857,  7.81632653,  8.06122449,  8.30612245,  8.55102041,\n",
       "        8.79591837,  9.04081633,  9.28571429,  9.53061224,  9.7755102 ,\n",
       "       10.02040816, 10.26530612, 10.51020408, 10.75510204, 11.        ])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xfit = np.linspace(-1, 11)\n",
    "xfit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "As before, we need to coerce these *x* values into a ``[n_samples, n_features]`` features matrix, after which we can feed it to the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:18.396074Z",
     "start_time": "2019-06-19T03:32:18.392526Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "Xfit = xfit[:, np.newaxis]\n",
    "yfit = model.predict(Xfit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Finally, let's visualize the results by plotting first the raw data, and then this model fit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:18.642629Z",
     "start_time": "2019-06-19T03:32:18.398793Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y)\n",
    "plt.plot(xfit, yfit);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Typically the efficacy of the model is evaluated by comparing its results to some known baseline, as we will see in the next example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Supervised learning example: Iris classification\n",
    "\n",
    "> ### Question: given a model trained on a portion of the Iris data, how well can we predict the remaining labels?\n",
    "\n",
    "For this task, we will use an extremely simple generative model known as **Gaussian naive Bayes** \n",
    "- which proceeds by assuming each class is drawn from an axis-aligned Gaussian distribution\n",
    "- see [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb) for more details).\n",
    "- it is so fast \n",
    "- it has no hyperparameters to choose\n",
    "\n",
    "Gaussian naive Bayes is often a good model to use as a baseline classification, before exploring whether improvements can be found through more sophisticated models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# To evaluate the model on data it has not seen before\n",
    "\n",
    "- we will split the data into a *training set* and a *testing set*.\n",
    "    - Using the ``train_test_split`` utility function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:18.652325Z",
     "start_time": "2019-06-19T03:32:18.645497Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "Xtrain, Xtest, ytrain, ytest = train_test_split(X_iris, y_iris,\n",
    "                                                random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "With the data arranged, we can follow our recipe to predict the labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:18.665125Z",
     "start_time": "2019-06-19T03:32:18.655173Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB # 1. choose model class\n",
    "model = GaussianNB()                       # 2. instantiate model\n",
    "model.fit(Xtrain, ytrain)                  # 3. fit model to data\n",
    "y_model = model.predict(Xtest)             # 4. predict on new data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Finally, we can use the ``accuracy_score`` utility to see the fraction of predicted labels that match their true value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:18.673066Z",
     "start_time": "2019-06-19T03:32:18.668102Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('setosa', 'setosa') ('versicolor', 'versicolor') ('versicolor', 'versicolor') ('setosa', 'setosa') ('virginica', 'virginica') ('versicolor', 'versicolor') ('virginica', 'virginica') ('setosa', 'setosa') ('setosa', 'setosa') ('virginica', 'virginica') ('versicolor', 'versicolor') ('setosa', 'setosa') ('virginica', 'virginica') ('versicolor', 'versicolor') ('versicolor', 'versicolor') ('setosa', 'setosa') ('versicolor', 'versicolor') ('versicolor', 'versicolor') ('setosa', 'setosa') ('setosa', 'setosa') ('versicolor', 'versicolor') ('versicolor', 'versicolor') ('versicolor', 'virginica') ('setosa', 'setosa') ('virginica', 'virginica') ('versicolor', 'versicolor') ('setosa', 'setosa') ('setosa', 'setosa') ('versicolor', 'versicolor') ('virginica', 'virginica') ('versicolor', 'versicolor') ('virginica', 'virginica') ('versicolor', 'versicolor') ('virginica', 'virginica') ('virginica', 'virginica') ('setosa', 'setosa') ('versicolor', 'versicolor') ('setosa', 'setosa')\n"
     ]
    }
   ],
   "source": [
    "print(*zip(ytest, y_model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:18.682628Z",
     "start_time": "2019-06-19T03:32:18.676008Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9736842105263158"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(ytest, y_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "With an accuracy topping 97%, we see that even this very naive classification algorithm is effective for this particular dataset!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Unsupervised learning example: Iris dimensionality reduction\n",
    "\n",
    "Reducing the dimensionality of the Iris data to more easily visualize it:\n",
    "- Iris data is four dimensional: \n",
    "    - there are four features recorded for each sample.\n",
    "\n",
    "The task of dimensionality reduction is to ask:\n",
    "\n",
    "> ## whether there is a suitable lower-dimensional representation that retains the essential features of the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Unsupervised learning example: Iris dimensionality reduction\n",
    "\n",
    "Dimensionality reduction is often used as an aid to visualizing data: \n",
    "- it is much easier to plot data in two dimensions than in four dimensions or higher!\n",
    "\n",
    "Here we will use ``principal component analysis`` (PCA; see [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb))\n",
    "- It is a fast linear dimensionality reduction technique.\n",
    "\n",
    "We will ask the model to return \n",
    "- two components\n",
    "    - a two-dimensional representation of the data.\n",
    "# Following the sequence of steps outlined earlier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:18.695069Z",
     "start_time": "2019-06-19T03:32:18.685086Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA  # 1. Choose the model class\n",
    "model = PCA(n_components=2)            # 2. Instantiate the model with hyperparameters\n",
    "model.fit(X_iris)                      # 3. Fit to data. Notice y is not specified!\n",
    "X_2D = model.transform(X_iris)         # 4. Transform the data to two dimensions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "To plot the results:\n",
    "- A quick way to do this is to insert the results into the original Iris ``DataFrame``, \n",
    "- use Seaborn's ``lmplot`` to show the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:19.260622Z",
     "start_time": "2019-06-19T03:32:18.698394Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 555.35x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context(\"talk\", font_scale=1.5)\n",
    "iris['PCA1'] = X_2D[:, 0]\n",
    "iris['PCA2'] = X_2D[:, 1]\n",
    "sns.lmplot(\"PCA1\", \"PCA2\", hue='species', data=iris, fit_reg=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "In the two-dimensional representation, the species are fairly well separated, even though the PCA algorithm had no knowledge of the species labels!\n",
    "\n",
    "A relatively straightforward classification will probably be effective on the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Unsupervised learning: Iris clustering\n",
    "\n",
    "Let's next look at applying clustering to the Iris data.\n",
    "\n",
    "> ### A clustering algorithm attempts to find distinct groups of data without reference to any labels.\n",
    "\n",
    "We will use a powerful clustering method called a ``Gaussian mixture model (GMM)``\n",
    "- more detail in [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb).\n",
    "- A GMM attempts to model the data as a collection of Gaussian blobs.\n",
    "\n",
    "We can fit the Gaussian mixture model as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:19.294539Z",
     "start_time": "2019-06-19T03:32:19.264081Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.mixture import GaussianMixture as GMM     # 1. Choose the model class\n",
    "model = GMM(n_components=3,\n",
    "            covariance_type='full')  # 2. Instantiate the model with hyperparameters\n",
    "model.fit(X_iris)                    # 3. Fit to data. Notice y is not specified!\n",
    "y_gmm = model.predict(X_iris)        # 4. Determine cluster labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "As before, we will \n",
    "- add the cluster label to the Iris ``DataFrame`` and \n",
    "- use Seaborn to plot the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:19.303266Z",
     "start_time": "2019-06-19T03:32:19.297074Z"
    },
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'axes.labelsize': 27.0,\n",
       " 'axes.linewidth': 1.875,\n",
       " 'axes.titlesize': 27.0,\n",
       " 'font.size': 27.0,\n",
       " 'grid.linewidth': 1.5,\n",
       " 'legend.fontsize': 24.75,\n",
       " 'lines.linewidth': 2.25,\n",
       " 'lines.markersize': 9.0,\n",
       " 'patch.linewidth': 1.5,\n",
       " 'xtick.labelsize': 24.75,\n",
       " 'xtick.major.size': 9.0,\n",
       " 'xtick.major.width': 1.875,\n",
       " 'xtick.minor.size': 6.0,\n",
       " 'xtick.minor.width': 1.5,\n",
       " 'ytick.labelsize': 24.75,\n",
       " 'ytick.major.size': 9.0,\n",
       " 'ytick.major.width': 1.875,\n",
       " 'ytick.minor.size': 6.0,\n",
       " 'ytick.minor.width': 1.5}"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.plotting_context()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:20.229768Z",
     "start_time": "2019-06-19T03:32:19.305736Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1275.35x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context(\"talk\", font_scale=1.5)\n",
    "iris['cluster'] = y_gmm\n",
    "sns.lmplot(\"PCA1\", \"PCA2\", data=iris, hue='species',\n",
    "           col='cluster', fit_reg=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "By splitting the data by cluster number, GMM algorithm recovered the underlying label  without an expert: \n",
    "- the measurements of these flowers are distinct enough\n",
    "- we could *automatically* identify the presence of these different groups of species \n",
    "    - with a simple clustering algorithm!\n",
    "- might further give experts in the field clues as to the relationship between the samples they are observing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Application: Exploring Hand-written Digits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "In the wild, this problem involves \n",
    "- locating characters in an image. \n",
    "- identifying characters in an image. \n",
    "\n",
    "Here we'll take a shortcut and use Scikit-Learn's set of pre-formatted digits, which is built into the library."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Loading and visualizing the digits data\n",
    "\n",
    "We'll use Scikit-Learn's data access interface and take a look at this data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:20.366432Z",
     "start_time": "2019-06-19T03:32:20.231989Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 8, 8)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "digits = load_digits()\n",
    "digits.images.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The images data is a three-dimensional array: \n",
    "- 1,797 samples \n",
    "- each consisting of an 8 × 8 grid of pixels.\n",
    "\n",
    "Let's visualize the first hundred of these:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:24.739140Z",
     "start_time": "2019-06-19T03:32:20.368949Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 100 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axes = plt.subplots(10, 10, figsize=(8, 8),\n",
    "                         subplot_kw={'xticks':[], 'yticks':[]},\n",
    "                         gridspec_kw=dict(hspace=0.1, wspace=0.1))\n",
    "\n",
    "for i, ax in enumerate(axes.flat):\n",
    "    ax.imshow(digits.images[i], cmap='binary', interpolation='nearest')\n",
    "    ax.text(0.05, 0.05, str(digits.target[i]),\n",
    "            transform=ax.transAxes, color='green')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "In order to work with this data within Scikit-Learn, \n",
    "- we need a two-dimensional, ``[n_samples, n_features]`` representation.\n",
    "- treating each pixel in the image as a feature: \n",
    "    - so that we have a length-64 array of pixel values representing each digit.\n",
    "- target array gives the previously determined label for each digit.\n",
    "\n",
    "Features and targets are represented as the ``data`` and ``target`` attributes in the `digits` dataset respectively:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:24.746938Z",
     "start_time": "2019-06-19T03:32:24.741566Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 64)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = digits.data\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:24.754923Z",
     "start_time": "2019-06-19T03:32:24.749995Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  5., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ..., 10.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ..., 16.,  9.,  0.],\n",
       "       ...,\n",
       "       [ 0.,  0.,  1., ...,  6.,  0.,  0.],\n",
       "       [ 0.,  0.,  2., ..., 12.,  0.,  0.],\n",
       "       [ 0.,  0., 10., ..., 12.,  1.,  0.]])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:24.762429Z",
     "start_time": "2019-06-19T03:32:24.757183Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797,)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = digits.target\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:24.770978Z",
     "start_time": "2019-06-19T03:32:24.765497Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, ..., 8, 9, 8])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "We see here that there are 1,797 samples and 64 features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Unsupervised learning: Dimensionality reduction\n",
    "\n",
    "We'd like to visualize our points within the 64-dimensional parameter space\n",
    "- it's difficult to effectively visualize points in such a high-dimensional space.\n",
    "- Instead we'll reduce the dimensions to 2, using an unsupervised method.\n",
    "\n",
    "Here, we'll make use of a manifold learning algorithm called *Isomap* (see [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb)), and transform the data to two dimensions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:26.679063Z",
     "start_time": "2019-06-19T03:32:24.774040Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 2)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.manifold import Isomap\n",
    "iso = Isomap(n_components=2)\n",
    "iso.fit(digits.data)\n",
    "data_projected = iso.transform(digits.data)\n",
    "data_projected.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "We see that the projected data is now two-dimensional.\n",
    "Let's plot this data to see if we can learn anything from its structure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:27.109357Z",
     "start_time": "2019-06-19T03:32:26.681770Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data_projected[:, 0], data_projected[:, 1], c=digits.target,\n",
    "            edgecolor='none', alpha=0.3,\n",
    "            cmap=plt.cm.get_cmap('nipy_spectral', 10))\n",
    "plt.colorbar(label='digit label', ticks=range(10))\n",
    "plt.clim(-0.5, 9.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "This plot gives us some good intuition into how well various numbers are separated in the larger 64-dimensional space. \n",
    "\n",
    "- zeros (in black) and ones (in purple) have very little overlap in parameter space.\n",
    "    - Intuitively, this makes sense: a zero is empty in the middle of the image, while a one will generally have ink in the middle.\n",
    "- There seems to be a more or less continuous spectrum between ones and fours: \n",
    "    - we can understand this by realizing that some people draw ones with \"hats\" on them, which cause them to look similar to fours.\n",
    "\n",
    "Overall, however, the different groups appear to be fairly well separated in the parameter space: \n",
    "- this tells us that even a very straightforward supervised classification algorithm should perform suitably on this data.\n",
    "\n",
    "Let's give it a try."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Classification on digits\n",
    "\n",
    "Let's apply a classification algorithm to the digits.\n",
    "\n",
    "- split the data into a training and testing set\n",
    "- fit a Gaussian naive Bayes model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:27.119553Z",
     "start_time": "2019-06-19T03:32:27.112982Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:27.138422Z",
     "start_time": "2019-06-19T03:32:27.122878Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "model = GaussianNB()\n",
    "model.fit(Xtrain, ytrain)\n",
    "y_model = model.predict(Xtest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Now that we have predicted our model, we can gauge its accuracy by comparing the true values of the test set to the predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:27.148437Z",
     "start_time": "2019-06-19T03:32:27.140680Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333334"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(ytest, y_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "With even this extremely simple model, we find about 80% accuracy for classification of the digits!\n",
    "\n",
    "However, this single number doesn't tell us *where* we've gone wrong\n",
    "- one nice way to do this is to use the *confusion matrix*, \n",
    "    - which we can compute with Scikit-Learn and plot with Seaborn:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:27.842746Z",
     "start_time": "2019-06-19T03:32:27.151710Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "sns.set_context(\"notebook\", font_scale=1.7)\n",
    "\n",
    "mat = confusion_matrix(ytest, y_model)\n",
    "\n",
    "sns.heatmap(mat, square=True, annot=True, cbar=False)\n",
    "plt.xlabel('predicted value')\n",
    "plt.ylabel('true value');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "This shows us where the mis-labeled points tend to be: \n",
    "- a large number of twos here are mis-classified as either ones or eights.\n",
    "\n",
    "Another way to gain intuition into the characteristics of the model:\n",
    "- to plot the inputs again, with their predicted labels.\n",
    "- using green for correct labels, and red for incorrect labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T03:32:32.191486Z",
     "start_time": "2019-06-19T03:32:27.845176Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 100 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(10, 10, figsize=(8, 8),\n",
    "                         subplot_kw={'xticks':[], 'yticks':[]},\n",
    "                         gridspec_kw=dict(hspace=0.1, wspace=0.1))\n",
    "\n",
    "test_images = Xtest.reshape(-1, 8, 8)\n",
    "\n",
    "for i, ax in enumerate(axes.flat):\n",
    "    ax.imshow(test_images[i], cmap='binary', interpolation='nearest')\n",
    "    ax.text(0.05, 0.05, str(y_model[i]),\n",
    "            transform=ax.transAxes,\n",
    "            color='green' if (ytest[i] == y_model[i]) else 'red')"
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    "Examining this subset of the data, we can gain insight regarding where the algorithm might be not performing optimally.\n",
    "\n",
    "To go beyond our 80% classification rate, we might move to a more sophisticated algorithm such as \n",
    "- support vector machines (see [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)), \n",
    "- random forests (see [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb)) \n",
    "- the other classification approaches."
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    "## Summary"
   ]
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    "In this section we have covered the essential features of the Scikit-Learn \n",
    "- data representation\n",
    "- the estimator API.\n",
    "\n",
    "Regardless of the type of estimator, the same import/instantiate/fit/predict pattern holds.\n",
    "\n",
    "Armed with this information about the estimator API, you can explore the Scikit-Learn documentation and begin trying out various models on your data.\n",
    "\n",
    "In the next section, we will explore perhaps the most important topic in machine learning: how to select and validate your model."
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    "<!--NAVIGATION-->\n",
    "< [What Is Machine Learning?](09.01-What-Is-Machine-Learning.ipynb) | [Contents](Index.ipynb) | [Hyperparameters and Model Validation](09.03-Hyperparameters-and-Model-Validation.ipynb) >"
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